Although the integration of large language models (LLMs) into robotics has unlocked transformative capabilities, it has also introduced significant safety concerns, ranging from average-case LLM errors (e.g., hallucinations) to adversarial jailbreaking attacks, which can produce harmful robot behavior in real-world settings. Traditional robot safety approaches do not address the novel vulnerabilities of LLMs, and current LLM safety guardrails overlook the physical risks posed by robots operating in dynamic real-world environments. In this paper, we propose RoboGuard, a two-stage guardrail architecture to ensure the safety of LLM-enabled robots. RoboGuard first contextualizes pre-defined safety rules by grounding them in the robot's environment using a root-of-trust LLM, which employs chain-of-thought (CoT) reasoning to generate rigorous safety specifications, such as temporal logic constraints. RoboGuard then resolves potential conflicts between these contextual safety specifications and a possibly unsafe plan using temporal logic control synthesis, which ensures safety compliance while minimally violating user preferences. Through extensive simulation and real-world experiments that consider worst-case jailbreaking attacks, we demonstrate that RoboGuard reduces the execution of unsafe plans from 92% to below 2.5% without compromising performance on safe plans. We also demonstrate that RoboGuard is resource-efficient, robust against adaptive attacks, and significantly enhanced by enabling its root-of-trust LLM to perform CoT reasoning. These results underscore the potential of RoboGuard to mitigate the safety risks and enhance the reliability of LLM-enabled robots.

本研究针对大语言模型（LLM）在机器人中应用所带来的安全隐患，提出了一种名为RoboGuard的双阶段护栏架构。该架构通过链式思维推理生成严格的安全规范，并利用时间逻辑控制合成解决潜在的安全冲突，从而大幅降低不安全计划的执行比例，显著提升了LLM驱动机器人的安全性和可靠性。

大语言模型驱动机器人安全护栏